Abstract:
Individuals differ widely in their capacity to remember
experiences from their past. Yet, little is known about the factors that
influence individual differences in autobiographical memory within the
population. Recently, healthy adults with a highly superior autobiographical
memory capacity have been identified (e.g., LePort, et al., 2012). Such
individuals have the uncanny ability to recollect their past in remarkably vivid
and accurate detail. In this talk, I will focus on healthy individuals with the
reverse pattern: a severely deficient autobiographical memory (SDAM) syndrome
with otherwise preserved cognitive function. Individuals with SDAM are unable to
re-experience specific events from their past, whereas they can learn and retain
factual information normally. I will present recent neuropsychological,
behavioral, anatomical (MRI), and functional (ERP and fMRI) data collected from
individuals with SDAM. The talk will conclude with a discussion about the
possible functional consequences of SDAM and the importance of studying
individual differences in autobiographical memory more broadly.

Abstract:
The media landscape has changed dramatically in recent decades and media consumers
have more options of what to consume and how to consume it than ever before.
Can media consumption behaviors affect cognition? This talk will examine both
the effects of consuming particular types of media (e.g., action video games)
and the effects of the manner of media consumption (singly or simultaneously).
Broadly, heavy action video game playing is associated with improvements in attentional
allocation and task switching, while heavy media multitasking is associated with
higher distractibility, leading to deficits in visual search and response inhibition.

Abstract:
Generalized Invariance Structure Theory (GIST) is a
mathematical and computational framework for the study of concept learning,
categorization behavior, and cognition in general that is grounded on a new
notion of invariance and on invariance pattern detection principles (Vigo, 2009,
2013, 2014). The core models of the
theory, including the fundamental law of invariance (aka GISTM), fit historical
and recent data (quantitatively and qualitatively) much more accurately than
competing models. In this talk, I shall give a basic introduction to GIST and
shall present cumulative and converging empirical evidence that shows that GIST
is a better account of human generalization, concept learning, and
classification behavior than competing models. Moreover, I shall discuss recent
empirical evidence that supports four candidate mathematical cognitive laws
derived from GIST. These candidate laws link concept learning and generalization
to four fundamental cognitive capacities: perception (Vigo, Barcus, and Zhang),
attention (Vigo, Zeigler & Halsey, 2013), decision making (Vigo & Doan, 2015),
and informativeness judgments (Vigo et al., 2014). Each candidate law accounts
for a high percentage of the variance in the data from its respective
experiments. Finally, I shall discuss several applications of GIST to other
domains ranging from neuroscience (Cai et al., 2014; Gao et al. 2016) to data
analysis (Vigo & Basawaraj, 2016) and from clinical psychology (Vigo, Evans,
Owens, 2015) to social cognitive science.

Abstract:
In my presentation I will review research conducted in our laboratory,
and the field in general, which has examined the extent to which fitness
training and physical activity enhances cognition and brain structure and
function of older adults. The presentation will cover both cross-sectional
and intervention studies of fitness differences and fitness and physical
activity training. Studies which assess cognition via both behavioral measures
and non-invasive neuroimaging measures, such as magnetic resonance imaging,
functional magnetic resonance imaging, event-related brain potentials, and
the event-related optical signal, will be reviewed and discussed. Finally,
I will explore the gaps in the human and animal literature on cognitive
and brain health and the manner in which they can be addressed in future
research.

Abstract:
Some of our most important scientific activities are conducted in
the absence of externally-imposed goals. We believe that releasing
investigators from the need to solve immediate problems will drive
long-term scientific evolution through the creation of unexpected
ideas, new needs to satisfy, and further questions to answer. Yet
we lack a common language to discuss these activities, which cover
a vast range of timescales and population sizes, from the speculative
after-dinner walk at a conference to the global scientific
Enlightenment itself. This makes it hard to see the similarities
that tie these processes together, the commonalities between the
problems they face, or the ways in which we might intervene to assist
their flourishing. To help remedy this, I present a new framework for
the quantitative study of scientific play, and apply it to an analysis
of twenty thousand papers in high-energy physics from the arXiv preprint
server. I compare the results of this analysis to a second
population-level study, of eighty years of poetry from a major American
poetry magazine, and to the work of an exemplar scientist, Charles
Darwin, and his interaction with the scientific community as a whole. New
information-theoretic methods allow us to see how these different activities
confront, and solve, the challenges of perpetual innovation. And they show
us how these forms of play lie on a continuum with, and can help inform,
more directed, micro-evolutionary scientific processes directed towards
the solving of a particular problem or material challenge.

Evidence from both linguistic theory and psycholinguistics
argues the lexicon contains many composite items, stored as such with their
internal structure. Moreover, one of
the tenets of the Parallel Architecture (Jackendoff 2002) is that there is no
divide between lexicon and grammar:
"rules of grammar" are stored in the lexicon in the form of schemas that contain
variables. Hence the traditional
overarching focus on how rules construct novel utterances must give way to a
shared focus, in which the relationships among lexical items are equally if not
more important.

Schemas come in two types.
A nonproductive schema captures regularities among its listed instances,
but it resists extension to new instances.
A productive schema also captures regularities among listed instances,
but in addition can be used freely to create new utterances online; it is this
function that corresponds most closely to traditional rules.

An important question arises, however:
Does the theory (or the brain) need nonproductive schemas?
Or can the subregularities encoded in nonproductive schemas be captured
by simpler associative principles, as advocated both by the connectionists and
by Pinker? I will suggest reasons
why nonproductive schemas might be helpful in acquisition, in organizing
storage, and in lexical access.